CMIP6 global models and their physical and numerical characteristics.
\r\n\t
",isbn:"978-1-83969-591-9",printIsbn:"978-1-83969-590-2",pdfIsbn:"978-1-83969-592-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"e39a567d9b6d2a45d0a1d927362c9005",bookSignature:"Dr. Umar Zakir Abdul Hamid and Associate Prof. Ahmad 'Athif Mohd Faudzi",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10778.jpg",keywords:"Model-Based Control, Optimal Control, Industrial Automation, Linear Actuator, Nonlinear Actuator, System Identification, Soft Robotics, Service Robots, Unmanned Aerial Vehicle, Autonomous Vehicle, Process Engineering, Chemical Engineering",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 25th 2021",dateEndSecondStepPublish:"March 25th 2021",dateEndThirdStepPublish:"May 24th 2021",dateEndFourthStepPublish:"August 12th 2021",dateEndFifthStepPublish:"October 11th 2021",remainingDaysToSecondStep:"21 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Umar Zakir Abdul Hamid, Ph.D. is an autonomous vehicle expert, and with more than 30 scientific publications under his belt, Umar actively participates in global automotive standardization efforts and is a Secretary for a Society of Automotive Engineers (SAE) Committee.",coeditorOneBiosketch:"Associate Professor Dr. Ahmad 'Athif Mohd Faudzi has more than 100 scientific publications as of 2021 and is currently leading a team of 18 researchers in UTM doing research works on control, automation, and actuators.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"268173",title:"Dr.",name:"Umar Zakir Abdul",middleName:null,surname:"Hamid",slug:"umar-zakir-abdul-hamid",fullName:"Umar Zakir Abdul Hamid",profilePictureURL:"https://mts.intechopen.com/storage/users/268173/images/system/268173.jpg",biography:"Umar Zakir Abdul Hamid, PhD has been working in the autonomous vehicle field since 2014 with various teams in different countries (Malaysia, Singapore, Japan, Finland). He is now leading a team of 12 engineers working in the Autonomous Vehicle Software Product Development with Sensible 4, Finland. Umar is one of the recipients for the Finnish Engineering Award 2020 for his contributions to the development of all-weather autonomous driving solutions with the said firm. He is an aspiring automotive thought leader and often invited as a guest and keynote speaker to industrial and technical events. 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He was a Visiting Research Fellow at the Tokyo Institute of Technology from 2015 to 2017. From March 2019 to date, he is the Director of the Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Malaysia. He is mainly engaged in the research fields of actuators (pneumatic, soft mechanism, hydraulic, and motorized actuators) concentrate his work in field robotics, bioinspired robotics and biomedical applications. He is a Professional Engineer (PEng), a Charted Engineer (CEng), a member of the IEEE Robotics and Automation Society (RAS) and a member of two Akademi Sains Malaysia Special Interest Group (ASM SIG) of Biodiversity and Robotics. He is also the recipient of Top Research Scientist Malaysia (TRSM) 2020 in the area of Robotics. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"71139",title:"Climate Models Accumulated Cyclone Energy Analysis",doi:"10.5772/intechopen.91268",slug:"climate-models-accumulated-cyclone-energy-analysis",body:'\nThe challenge of connecting climate change to tropical cyclones (TCs) lies in determining that a change has occurred given natural variability whether by significant changes in climate forcing such as greenhouse gases (GHGs) or aerosols or by the sum of both natural and anthropogenic factors.
\nTropical cyclone activity has complex characteristics that make it difficult to achieve robust future projections. The onset, duration, intensity, and phenomenology associated with these storms carry many uncertainties in numerical modeling, due to limitations of models to represent local/micro-scale physical processes and tangents to the computational aspect in simulating the climate of long periods, from decades to centuries.
\nChanges in natural variability, volcanic emissions, and solar activity have made a small contribution to the changes in climate over the last century [1, 2]. The natural cycles observed in climate records do not explain the increases in the heat content of the atmosphere, ocean, or cryosphere since the industrial age [3, 4, 5, 6].
\nEarth’s climate has been affected by changes in factors that control the amount of energy entering and leaving the atmosphere. These factors, known as radiative forcings, include changes in albedo through land use and cover, greenhouse gases, and aerosols. The increase in the concentration of greenhouse gases by emissions from human activities is the largest of these radiative forcings. By absorbing longwave radiation emitted by Earth and redirecting it equally in all directions, greenhouse gases increase the amount of heat retained in the climate system, warming the planet [2, 7, 8, 9].
\nA comparison of a model’s simulation of internal multidecadal climate variability with the observed increase in an Atlantic hurricane rapid intensification metric (1982–2009) finds a highly unusual behavior in the metric result and is consistent with the long-term response sign expected by the model to anthropogenic forcing [10]. In the same direction, the 2018 US National Climate Change Assessment reports that decreases in air pollution and increases in GHGs have contributed to increases in Atlantic hurricane activity since 1970 [11].
\nThere is growing evidence of a significant increase in the TC’s proportion that become major hurricanes, although the frequency of TCs has remained roughly constant in recent decades [12, 13, 14, 15, 16, 17]. A recent study showed that in the central and eastern tropical Atlantic basin during 1986–2015, the 95th percentile of 24 h intensity changes increased significantly [18]. The intensification rate of intensifying storms, another metric that is not dependent on TC frequency, exhibited significant growth during 1977–2013 in the West Pacific basin [19]. In both studies, the large-scale environment became more conducive to TC intensification over time. Areas with increases in potential intensities [20] and the largest increase in sea surface temperatures (SSTs) seem to be located with the largest positive changes in intensification rates.
\nHow future anthropogenic warming can affect TC is an important issue, mainly due to the large social impacts they can cause [21], as discussed in previous reports of the Intergovernmental Panel on Climate Change (IPCC) [22] and World Meteorological Organization (WMO) [23].
\nThe IPCC-AR5 [24] concludes for a 2°C global warming that there is more than 66% likelihood to the TC rainfall rates increase in the future and accompanying increase in atmospheric moisture content. Modeling studies on average indicate increase rainfall rates averaged within about 100 km of the storm by 10–15%. The TC intensities increase on average (1–10%), which would imply an even larger increase of percentage in the destructive potential per storm, assuming no reduction in storm size (responses to anthropogenic warming are uncertain).
\nThe future projection for the global number of Category 4 and 5 storms is likely to increase due to anthropogenic warming over the twenty-first, but there is less confidence since most modeling studies project a decrease (or little change) in the overall frequency of all combined TC [24].
\nLinks between climate and tropical cyclones were analyzed in [25], with a good understanding of the relationship at various time scales, with significant trends observed for cyclone intensity and frequency over the past decades over Atlantic. Most climate models simulate fewer tropical cyclones and stronger storms, with increase in precipitation rates. Further sea level rise is likely to increase storm threats, with studies of combined effects of floods and storms projecting that increases are due to global warming [26].
\nGiven the importance of tropical cyclone study, and how changes induced by human actions in the terrestrial system may affect such phenomena, the aim of this study is to evaluate simulations of global numerical models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) [27], by representing the recent past, and thus access future projections that may occur and indicate trends of changes in cyclone events.
\nACE uses the maximum wind speed over time to quantify hurricane activity by season, defined as the sum of the squares of the maximum wind speeds at 6-h intervals, considering the time while the hurricane is at tropical storm strength or greater [28]. As kinetic energy is proportional to the square of velocity, ACE is a value proportional to the energy of the system, by adding together the energy per some interval of time.
\nA review by [29] evaluates different hurricane indexes, indicating ACE as a valuable metric for quantifying the overall impact of tropical cyclones on the Earth’s climate, classifying this index as a duration-based integral of a time series.
\nThe ACE definition given by [28, 29] was adapted to use the monthly output from models, setting a related ACE:
\nwhere \n
The primary energy source for TC is the heat from the evaporation that comes from the warmed ocean surface; several studies showed the correlation between sea surface temperature and TC [21, 23, 30, 31]. Additionally, the increase in precipitation rates is largely based on the Clausius-Clapeyron ratio, which produces about a 7% increase in water vapor in the atmosphere by 1°C warming [32, 33]. Thus, the maximum near-surface air temperature at 2 m (TASMAX) expresses a direct physical relationship with the TC occurrence, used here to be an auxiliary proxy to help the discussion ahead.
\nMost tropical cyclones are formed in the intertropical convergence zone (ITCZ). Tropical waves are another important source of atmospheric instability, contributing to the development of about 85% of cyclones over the Atlantic Ocean [35, 36]. TC rarely forms or moves around 5° from the equator where the Coriolis effect is more weak, with most of them appearing between 10 and 30° latitude away from the equator [37]. Thus, the delimited area in the central region in Figure 1 was chosen as representative to develop the objective of this work.
\nTropical cyclone tracks map (adapted from [34]) with the region delimitation for this study: 75 W to 45 W and 13 N to 25 N.
Climate models have been used to understand how the climate has changed in the past and may change in the future. These models simulate the physics, chemistry, and biology of the atmosphere, land, and oceans, now called Earth system models, and require supercomputers to generate their climate projections.
\nA set of standard experiments was designed for CMIP, allowing results to be comparable across different model simulations, to see where models agree and disagree on past and future scenarios [38].
\nCMIP6 historical experiment covers the period 1850–2014, forced by datasets that are largely based on observations, used as an important benchmark for assessing performance through evaluation against observations, and are well suited for quantifying and understanding important climate change response characteristics [38, 27]. The characteristics and forcings included in historical were described in [27]:
Emissions of short-lived species and long-lived GHGs
GHG concentrations
Global gridded land use forcing datasets
Solar forcing
Stratospheric aerosol dataset (volcanoes)
AMIP sea surface temperatures and sea ice concentrations (SICs)
For simulations with prescribed aerosols, a new approach to prescribe aerosols in terms of optical properties and fractional change in cloud droplet effective radius to provide a more consistent representation of aerosol forcing
For models without ozone chemistry, time-varying gridded ozone concentrations and nitrogen deposition
Shared socioeconomic pathway (SSP) scenarios are part of a framework designed to span a range of futures in terms of the socioeconomic challenges that they imply for mitigating and adapting to climate change. In short they are:
SSP1 - Low challenges to mitigation and adaptation.
SSP2 - Intermediate challenges to adaptation and mitigation.
SSP3 - High challenges to mitigation and adaptation.
SSP4 - Low challenges to mitigation and high challenges to adaptation.
SSP5 - High challenges to mitigation and low challenges to adaptation [39, 40].
SSP585 results of a complementary effort by SSP narrative and the Representative Concentration Pathways (RCPs), representing the high end of the range of future pathways. SSP5 was chosen for its forcing pathway because its emissions pathway is high enough to produce a radiative forcing of 8.5 W/m2 by the end of the century, updating RCP8.5 [39, 40]. Figure 2 summarizes all the current SSP scenarios for CMIP6 in terms of radiative forcing.
\nAnthropogenic radiative forcing for the twenty-first-century scenarios in the ScenarioMIP design (from [39], shown in [40]).
Climate reanalysis aims to assimilate historical observational data with numerical models to generate consistent time series of multiple climate variables. These are a comprehensive description of the observed climate as it has evolved during recent decades, providing global datasets at sub-daily intervals, turning possible more detailed approaches, and then having just observation data [41, 42].
\nNCEP-DOE Reanalysis 2 project performs data assimilation using past data from 1979 through the present. The data is available at PSD portal (
The zonal and meridional wind components at 2 m and 6-6 hs data were used to compute monthly maximum wind speed. The ACE index was obtained by applying this monthly maximum wind speed in Eq. 1. Similarly, the monthly maximum temperature (TASMAX) was calculated through the daily maximums obtained with 6-6hs data. These variables provided by NCEP reanalysis were used as a reference to evaluate the recent past simulations.
\nCMIP6 simulation outputs are available in the Earth System Grid Federation (ESGF), through a distributed data archive developed. The data are hosted on a collection of nodes across the world by modeling centers [43]. The main portal to access the datasets is
The complexity of the models, experiments, and methodologies makes it hard for modeling centers to complete the entire archive to participate in CMIP6. Thus, at this time the available datasets to use in this work, for both historical and SSP585, are listed in Table 1.
\nModel | \nRun | \nNominal resolution | \nVertical levels | \nComponents | \n
---|---|---|---|---|
CNRM-CM6-1 | \nr1i1p1f2 | \n250 km | \n91 | \nAOGCM | \n
CNRM-ESM2-1 | \nr1i1p1f2 | \n250 km | \n91 | \nAOGCM/BGC/AER/CHEM | \n
IPSL-CM6A-LR | \nr1i1p1f1 | \n250 km | \n79 | \nAOGCM/BGC | \n
MPI-ESM1-2-HR | \nr1i1p1f1 | \n100 km | \n95 | \nAOGCM | \n
\n | Institution/Center | \nReference | \n||
CNRM-CM6-1 | \nCNRM (Centre National de Recherches Meteorologiques, Toulouse 31,057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31,057, France) | \n[44] | \n||
CNRM-ESM2-1 | \n[45] | \n|||
IPSL-CM6A-LR | \nInstitut Pierre Simon Laplace, Paris 75,252, France | \n[46] | \n||
MPI-ESM1-2-HR | \nMax Planck Institute for Meteorology, Hamburg 20,146, Germany | \n[47] | \n
CMIP6 global models and their physical and numerical characteristics.
The models simulate their own climate, with no obligation to get it right exactly when specific events have occurred in relation to observational data. On the other hand, they should be able to represent global or large-scale phenomena such as El Niño, La Niña, ITCZ, and ocean circulation. Thus, the models are expected to represent the average climate of the recent past, as well as to simulate the future in the same direction.
\nThus, the regional annual cycle for variables with approximately linear behavior, such as temperature, should be easier to represent. Episodic variables such as precipitation and local wind speed are more difficult to model numerically, given the randomness of events. But it is expected that for long periods, good results will be obtained from the models on average terms, as suggested by the WMO to use at least 30 years for climate studies.
\nThe ACE, because it depends directly on the wind, can be assumed to present results that are less well behaved concerning the reference data than the temperature. This occurs in the results obtained here through the CMIP6 models; the ACE index shows similarities for the monthly climate average through the annual cycle (Figure 3), although with a discrepancy between the models higher than the maximum temperature (Figure 7). In addition to the nonlinearity involved, the models themselves have their limitations, which may be due to the physical, numerical, or computational approach. Model scaling errors for the ACE index are in the order of −12%. Among the models, MPI-ESM1-2-HR performed better in representing the annual ACE cycle, with a good approximation of the mean monthly values compared to reanalysis, with a correlation of 0.93 and bias error − 1.28%.
\nAnnual cycle of the study region for ACE.
The French model IPSL-CM6A-LR has the highest percentage error among the others for ACE, at 22.70% of the reanalysis (Figure 3). On the other hand, this same model obtained a better representation of seasonal variability (0.85 correlation) than the two CNRM models, which presented smaller errors (−11%) but with lower correlations, 0.76 (CNRM-CM6-1) and 0.74 (CNRM-ESM2-1). The critical value of the sample correlation, for 95% significance (n-2 degrees of freedom), is 0.576, with all model results performing significant correlations.
\nThe variation coefficient (VC), defined as the ratio of standard deviation by the mean, represents the relative standard deviation, used here to assess whether the models have significant monthly interannual variability or whether they represent climate more closely than stationarity.
\nThe months with the highest percentage variation range from December to May (Figure 4), where there is a relative skill of the models, VC values not exceeding 5% from NCEP-DOE Reanalysis 2. In the months from June to November, models have more difficulties to simulate the maximum wind speeds, possibly resulting from the higher activity of the ITCZ in the region selected for the study and being also the months with the high temperatures of the year (Figure 7). The MPI-ESM1-2-HR model best quantified the interannual ACE variations for the months with the highest CT activity, followed by the IPSL model, erring only in magnitude, hitting the temporal evolution in most months.
\nPercentage variation coefficient for ACE monthly values.
The polynomial curve fitting creates an approximating function that attempts to capture important patterns in the data while leaving out noise or other fine-scale structures/rapid phenomena. This method can aid in data analysis by being able to extract more information from the data as long as the assumption of smoothing is reasonable and to provide analysis that is both flexible and robust.
\nThe first-degree coefficient represents the linear trend of the data, and, as shown in Figure 5, the NCEP reanalysis has a small negative trend in annual ACE over the recent past (1979–2014). With the same trend signal, IPSL-CM6A-LR follows the observation pathway, while the other three models simulate a positive trend. From the second-degree coefficient to further ahead, the adjustments are related to patterns with more oscillatory rates, and in the present analysis, this type of signal has no significance. Thus, it can be assumed that models with coefficient values close to reanalysis, in modulus, should have a similar pattern of variability in different modes. The German model was the most difficult to obtain the adjustment, probably because it has a higher horizontal resolution, making it possible to discretize more climate phenomena, which has coefficient values more distant than the obtained for the reanalysis.
\nPolynomial adjustment coefficients for annual ACE of the study region.
The projection of annual ACE for the twenty-first century (Figure 6) has a similar average behavior among models, without abrupt trend changes, presenting modes of variation not far from the simulated for the recent past. The long-term trend for the period 2065–2100 is an increase in the average annual ACE values for the CNRM-ESM2-1 and MPI-ESM1-2-HR models and a reduction for the IPSL-CM6A-LR and CNRM-CM6-1, with no majority agreement.
\nAnnual ACE time series for recent past and future simulation under SSP585.
The TASMAX annual cycle has a good performance by the models; in terms of seasonality, all models show suitable patterns, with low errors in representing the evolution of the monthly cycle. The bias error is a problematic aspect, the three French models have sub estimate ~2°C, while MPI-ESM1-2-HR fits almost the entire NCEP reanalysis climatology (Figure 7).
\nAnnual cycle of mean over the study region for TASMAX.
The annual TASMAX projections for the future (Figure 8) are similar to that described in the IPCC Special Report on the impacts of global warming of 1.5°C above preindustrial levels and related global greenhouse gas emission pathways [48], in which there is a high confidence that the estimated anthropogenic global warming is currently increasing at 0.2°C per decade due to past and ongoing emissions.
\nAnnual TASMAX time series for recent past and future simulation under SSP585.
The mid- and long-term ACE future projections for most models analyzed indicate to the increase of the index and just the MPI-ESM1-2-HR follows a different pathway (Figure 9). The approaches used in the results shown in Figures 9 and 10 consist of calculating the future percentage change over the periods and applying this change to the reanalysis recent past value. This way, the projection has no bias error associated to it, bringing the right value expected in the future for the projection.
\nFuture ACE projection under SSP585 for mid (2020–2055) and long (2065–2100) terms.
Future TASMAX projection under SSP585 for mid (2020–2055) and long (2065–2100) terms.
The model with better results, MPI-ESM1-2-HR, trends to increase annual ACE under the projection period, but points the opposite to mid- and long-term mean (Figure 9). One of the changes in the annual cycle is an increase in the index in months where TC activity is not intense, as in the months of the beginning and end of the year, in which there is also an increase in VC. These factors suggest that the seasons and their interannual variations in cyclonic activity will be affected by the forcing on the climate system, in this case, under the scenario of high GHG emissions and high challenges to mitigation SSP585.
\nThe MODELS-MEAN projection (Figures 9 and 10) was computed by the weight mean, considering the annual cycle correlation value as the weight for each model. Thus, MODELS-MEAN performs a more confident projection. The results for that concern to a future with more chances of facing more tropical cyclone activity, plus the huge long-term TASMAX increase of 3.1°C (Figure 10); the twenty-first century may experiment more heavy cyclones and stronger storms with more frequency, as indicated by other studies [21, 23, 25, 26].
\nThe accumulated cyclone energy index adapted for this work has made it simpler to assess the recent past and to obtain projections of CMIP6 models, given the use of monthly data directly.
\nIn the present climate evaluation (1979–2014), reasonable results were obtained for the ACE index; the French models of lower horizontal and vertical resolution showed more difficulties to represent the index, while the Max Planck Institute model demonstrated ability to simulate the climate with more accuracy than the others, presenting values of both ACE and TASMAX very close to NCEP Reanalysis 2.
\nTASMAX was already expected to obtain good results numerically; in terms of seasonality all models show suitable patterns, with low errors in representing the evolution of the monthly annual cycle.
\nThe annual ACE projection has a similar average behavior among models in the recent past, without abrupt trend changes, but with no major agreement to increase or reduce trend. The mid- and long-term mean for most models analyzed shows an increase in ACE.
\nThe MPI-ESM1-2-HR projections suggest that the seasons and their interannual variations in cyclonic activity will be affected by the forcing on the climate system, in this case, under the scenario of high GHG emissions and high challenges to mitigation SSP585.
\nThe results indicate to a future with more chances of facing more tropical cyclone activity, plus the mean increase of 3.1°C in maximum daily temperatures, and more heavy cyclones and stronger storms with more frequency may be experimented, as indicated by other studies [21, 23, 25, 26].
\nThe study needs to be expanded, including more models, to increase the range of results and to narrow down potential trends that may occur in ensemble analysis.
\nIn the most recent the environmental provident and threatening conduct of arsenic has increased the consideration of the world due to its pollution and hazardous effects throughout the world [1, 2]. Arsenic contamination is serious issue throughout the world and is substantial risk factor in most of countries including China, U.S.A, India, Pakistan, Bangladesh, Mexico and Argentina. Human revelation to arsenic is through oral route involving food and water or through inhalation of agricultural pesticides [3, 4, 5]. According to World Health Organization fact sheet, arsenic contamination is major public issue requires emergency amendments [6]. As the arsenic contamination of ground water is most serious issue for human health in China, India, Pakistan, inner-Mongolia and Bangladesh [7]. Arsenic is present round the earth in environment and is extremely toxic for life. It is metalloid occurring 20th in earth crust, 14th in sea water and 12th in human body [8]. Toxic effects of arsenic on health is wide spread in both humans and animals [9], as epidemiological substantiation proved that chronic arsenic exposure is associated with increased risk of liver, bladder and skin cancer, cardiovascular diseases, diabetes mellitus neuropathies, and ocular diseases [10, 11, 12]. Arsenic ingestion leads to accumulation in liver, kidney and lungs and small amount in gastrointestinal tract, muscle nervous system and spleen because these organs are rich in oxidative enzymes [13]. The toxic effects of arsenic mostly occur from chronic exposure to humans and animals. Epidemiological studies have revealed that chronic arsenic exposure is associated with elevated risk of liver, lung, kidney, and skin cancer in addition to other ailments such as vascular, diabetic, reproductive and neurologic [14, 15]. On the contrary arsenic has been considered as an effective chemotherapeutic agent in the treatment of human cancer [16]. Various experimental models have been developed to understand the diseases caused by arsenic exposure. However reproductive and developmental toxicity have been poorly understood. Numerous studies documented elevated spontaneous abortion and stillbirth and decreased birth weight by utero arsenic exposure [17]. Arsenic as a risk factor for developing fetus has primarily been studied through murine studies, signifying the reproductive toxicity of arsenic. In animal’s studies on arsenic toxicities revealed that arsenic is associated with spermatotoxicity [18] inhibition of testicular steroidogenesis and reduction of weight of testes and accessory organs [19]. In the current review, we try to summarize the existing information on arsenic toxicity from the available literature. We initiate by describing how and when the arsenic contamination took place by considering the course through current literature lens. We present an overview of how human and animals have been affected in the light of colors of various exposure sources by considering the relationship between arsenic toxicity and environment influenced by human activities. Furthermore, we conclude with a preview of future directions and challenges for this field.
Endocrine Disruption.
The gene regulation of mineralocorticoid, glucocorticoids, and androgen and progesterone receptors is disrupted by arsenic [20]. The mechanistic effect of arsenic on these four steroid hormones is studies on glucocorticoids receptors. Arsenic altered receptor of transcription regulation of DNA dependent glucocorticoids, signifying that transcriptional machinery is required for glucocorticoids regulation [21]. Comprehensive mutational investigation of glucocorticoids revealed that only receptor is not the causal target for arsenic effect, as studies that entire C-terminal and N-terminal domains can be removed from glucocorticoids receptors without altered arsenic effects, which indicate the primary mediator of the response of central DNA binding domain. However mutation of almost all the predicted sites of DNA binding domains did not eliminate function and also did not ablate the arsenic effects [21] Abnormalities of male reproductive system such as hypospadias, prostate, testicular cancer and cryptorchidism, may instigate through endocrine disruption [19].
Male reproductive system is directly affected by arsenic exposure, as it targets particular reproductive organs and neuroendocrine system and it also disrupt sertoli cells during fetal development. Sertoli cells propagate during prepubertal, fetal, neonatal period and these stages are chiefly susceptible to adverse effects of arsenic (Figure 1) [22]. The interruption of spermatogenesis at cell differentiation stage can decline the overall sperm count, and cause sperm DNA damage [23]. Arsenic accumulation in seminal vesicles, prostate and epididymis reduces the progressive sperm motility [24]. Beyond this arsenic also cause hormonal disturbance through affecting endocrine system, disturbing the secretion of androgen from leyding cells, it has significant association between arsenic exposure and sperm motility in arsenic exposed patients [24]. Environmental epidemiological evidences show that in general environmental conditions there is association between arsenic exposure and sperm quality in male [25]. The total arsenic concentration and sperm concentration are strongly correlated in the in the seminal plasma of heavily exposed human population [26]. The quality of semen of arsenic exposed population is decreased and there was a strong association between sperm percentage of the group exposed by arsenic, as the sperm concentration was lower in arsenic exposed group than non-exposed group [27].
Reproductive toxicity of arsenic.
The interference in spermatogenesis at cell differentiation stage can reduce the overall sperm count, increased anomalous sperms, and impaired constancy of sperm [28]. As accumulation of arsenic in seminal vesicles, seminal fluid, prostate, and epididymis may impair the sperm progressive motility [29]. In addition arsenic causes hormonal disproportion affecting neuroendocrine system and androgens, as there is strong evidence that oxidative stress vulnerably affect the spermatozoa due to extreme production of reactive oxygen species resulting in the peroxidation of poly unsaturated fatty acids in the plasma membrane [30]. Arsenic increase the reactive oxygen species production and decrease the glutathione, and other antioxidant level which lead to lipid per oxidation of cell membrane causing apoptosis leads to oxidative DNA damage [31, 32]. Damage of sperm membrane reduces sperm motility and ability to fuse with oocyte, whereas the sperm DNA damage compromise parental genomic involvement to the embryo [33] and increase the risk of infertility, and serious disease in offspring [34].
In addition to affecting sperm quality, some epidemiological studies documented that arsenic exposure in the environment is increasing the sterility risks in populations which result in decrease androgen hormones level in body, sexual dysfunction and chromosomal aberration (Figure 2) [35]. As level of hormones and arsenic concentration is measured in the blood of infertile males which indicated that the concentration of arsenic and blood luteinizing hormones are strongly negatively correlated. LH can stimulate testosterone production in interstitial cell, the dysfunction or absence of testosterone lead to male infertility [36]. Epidemiological studies revealed that in Taiwan due to drinking of arsenic contaminated water the risk of prostate cancer is 6 times more than other population [37]. In many studies it is documented that risk of arsenic exposure affect genetic integrity in chromosome repeat region and it has certain effect on Y chromosome [37]. A group reported that arsenic exposure may increase erectile dysfunction; the experimental showed that the risk of erectile dysfunction was 3.4 fold higher in arsenic exposed population [38].
Genotoxicity of arsenic adapted from [39].
Recent data has summarized toxicological effects on female reproductive system in humans and animals implicating impaired fertility effects [40]. Infertility has been predicted as substantial public health hazard and becoming medical challenge round the globe [41], as it ahead of any uncertainty that lifestyle and quality of ambient environment can play fundamental role in reproductive success in both human and animal population [42]. It is demonstrated that exposure to toxic metals such as arsenic, lead and cadmium may be extremely involved in impaired fertility [43]. Arsenic is highly toxic and hazardous for pregnant humans and animals because it can disrupt the neuroendocrine system as it may inhibit estrogen binding receptors and un-regulate the progesterone receptors and it is potential source of estrogen dependent diseases such as breast cancer, endometritis and spontaneous abortions in human population [44]. Elevated endometrial cancer risk is associated with intake of arsenic [45]. Arsenic exposure may also affect angiogenesis in endometrium during pregnancy which is the most important for embryogenesis. These ailments lead to endometrial dysfunction, premature birth, subfertility, sterility and spontaneous abortions [17].
Arsenic is well recognized for its reproductive toxicity, as in case of male reproductive system it is accounted that to hinder activities of spermatogenetic enzymes and impede spermatogenesis [28]. Arsenic may act on brain or pituitary or and on germ line cells and affect the female reproductive system such as it reduce ovarian steroidogenesis, prolong diestrus, degenerate ovarian follicles and decrease the plasma level of estradiol and progesterone [46]. Furthermore reduced plasma gonadotrophin level may decline activities of ovarian 3β- HSD (Hydroxysteroid dehydrogenase) and 17β- HSD (Hydroxysteroid dehydrogenase), which are essential regulatory enzymes for steroidogenesis [47]. As it is observed that low plasma level of estradiol may be the cause of diestrus. Furthermore, arsenic exposure in human causes reproductive toxicity, including elevated incidence of miscarriages, still birth and low birth weight in offspring [17]. Similarly, it also effect on viability in the conceptus, dam mortality and weight gain of fetus [48]. Arsenic plays a potential role in disruption of female hormonal function, such as interfering hormone synthesis and hormone normal function. All hormones are differing in their structure and function and have various routes of synthesis with numerous steps. Arsenic exposure through pesticides and other products may disrupt the chain of hormone synthesis such as inhibition of estrogen biosynthesis [49], by preventing the conversion of androgen into estrogen [50]. Methylated arsenic may interfere in dopamine beta hydroxylase activity resulting in reduced conversion of dopamine into nor-epinephrine [19] which may lead to hindrance of hypothalamic catecholamine activity involved in generation of pro-estrus surge in LH, which stimulates ovulation [51]. It also inhibits various other enzymes which are involved in progesterone synthesis [52]. Disruption in LH timing surge could alter the viability and quality of oocytes [51] and inhibition of progesterone secretion may lead to poor conception (Figure 1) [48]. The distorted estrogen signaling may cause over expression of estrogen receptors through promoter region hypo-methylation and cause epigenetic change to produce estrogen like effect by direct or indirect stimulation of estrogen receptors.
Inorganic arsenic affect the nervous system causing behavioral changes and peripheral neuropathies [53], as chronic exposure of arsenic during pregnancy may affect fetal brain development as a result mutilation of behavioral skills, including cognitive abilities and social competency. It is further conformed that exposure of chronic arsenic increase the risk of spontaneous abortions and stillbirths [54]. Significant association of arsenic exposure was found during pregnancy causing spontaneous abortions and stillbirth [3, 55]. It was reported that the elevated the risk of still birth and neonatal mortality amongst 200 married women in Bengal [55, 56]. All pregnant women were provided proper care in arsenic exposed area, showed significant association between arsenic concentration and birth defects. In the recent study spontaneous abortions and still births were observed between exposed and unexposed women, which included 240, women living in arsenic exposed area in West Bengal of India with high concentrated arsenic drinking water [55, 57] as well as [58] documented the most common arsenic exposed regions in West Bengal, and miscarriage was observed due to arsenic contaminated water. However spontaneous abortions and still births were observed in almost all the arsenic exposed areas throughout the world [55]. Furthermore, a hospital based study was conducted in Texas community with low level of arsenic exposure through inhalation primarily arsenic based agricultural products reported spontaneous abortions and stillbirths [59].
According to WHO documentation more than 10% of women are at the risk of infertility through the exposure of heavy metals such as arsenic which are the major environmental contaminant which may cause reproductive disorders [60]. WHO surveyed that the problem of infertility was pre dominantly greater in female than in males. Ovulation disturbances account for common cause of sub fertility in women [61, 62], as ovulation disturbances are present in uneven or lacking menstrual periods and can overcome through reproductive hormones. The risk of infertility increased in women due to hormonal disturbance, delay ovulation, chromosomal aberration in oocytes by higher exposure level of toxicity. Hormonal imbalance is an important cause of infertility in females due to endocrine disruption by arsenic toxicity which is the major cause of infertility in females (Figure 2) [40]. It may also cause cycle abnormality, such as decline in estrus cycle number and elevated duration of diestrus [63]. Ovulation issues, endocrine interference with estrogenic properties may inhibit ovulation and the mid cycle LH surge from pituitary gland in females which may lead to female fertility problems [40, 64]. However, most studies revealed that the arsenic exposure through pesticides and insecticides is the major cause of infertility in females, as these decrease the number of mature follicles and elevate the number of atretic follicles and this indicates potential reduction in fertility [65]. Increased exposure to methylated arsenic may lead to decrease in uterus weight which may affect implantation and increase pre-implantation embryonic loss which leads to infertility in females [66]. A recent study revealed that the women exposed to pesticides have longer menstrual cycle and increased probability of missed periods, as studied in USA; infertile women were observed have three times more exposure to pesticides, in which whole chain of gametogenesis is affected [67].
Several studies have documented the elevated inter individual variability in receptiveness of arsenic toxicity underlying genetic factor as a cause of variability. The genotoxicity of arsenic cause deoxyribonucleic acid modification such as chromosomal aberrations, mutation, micronuclei formation, deletion, sister chromatid exchange [68]. Numerous studies have been done to explain the genotoxic effect of arsenic, over and above stimulation of oxidative stress and distorted DNA repair [69]. For the purpose of understanding several studies confirmed the manipulation of genetic polymorphism in gene coding enzymes involved in mechanism of arsenic metabolism and detoxification [70]. It has been demonstrated that arsenic does not affect DNA directly and is considered a poor mutagen, as regardless of its low mutagenicity it affects the mutagenicity of other carcinogens. For illustration, an elevated increase in mutagenicity of arsenic with ultraviolet light has been observed in mammalian cells [71]. Progression of experimentation proposed that arsenic genotoxicity is associated with the generation of reactive oxygen species during its biotransformation [68]. The generation of reactive oxygen species is able to break DNA strands, cross links and chromosomal aberration [72]. One of the mechanisms of arsenic destroys to DNA is base adjustment in particular 8-oxoguanine is one of the most frequently formed DNA nuclease modifications which are a mutagenic miscoding lesion that lead to G: C to T: A transverse [73].
Moreover arsenic can induce DNA strand breaks even at low concentration [70], as single strand breaks are caused by reactive oxygen species on DNA base directly or indirectly during base excision repair mechanism [74]. As it was observed that human fibroblast cells demonstrate single strand break and chromatid substitute interfering with polyadenosinediphosphate ribose polymerase activity which is a protein important for single strand DNA break and double strand DNA break repair process (Figure 2) [75]. Recent studies revealed that chronic arsenic exposure induces oxidative DNA damage, reduced thymic functions and subsequent immunosuppression in childhood [76]. Arsenic is well known inducer of chromosomal aberration which involves both clastogenic and a euploidogenic [77]. Recent studies documented cytogenetic monitoring by using chromosomal aberration and micronuclei assay in order to observe genotoxic effects of arsenic in human and animal population [78]. Inhibition of DNA repair is considered one of the most important effects of genotoxicity of arsenic. Nucleotide excision repair and base excision repair are the two process of DNA repair which are inhibited by reactive oxygen species of arsenic [79]. Earlier studies revealed that arsenic exposure may hinder the nucleotide excision repair mechanism of DNA repair but in recent studies it is observed that it also inhibit the base excision repair mechanism (Figure 2) [80]. Changes in DNA repair mechanisms have been confirmed in human exposed population, as arsenic exposure was linked with reduced expression of excision repair to at low dose. They have found that arsenic metabolites can affect several processes in the cell [81, 82, 83]. Particularly cellular activity of human 8-oxoguanine DNA glycosylase was the most sensitively affected by dimethylmonoarsenic acid [80]. Recently, epidemiological studies revealed that arsenic may affect single nucleotide polymorphism in genes of DNA repair pathways [84]. Arsenic causes DNA damage and changes cellular capacity for DNA repair. Consequently alterations in DNA repair capacity is associated to the presence of polymorphisms in DNA repair genes which are related to risk of developing disturbance induced by arsenic [85].
One of the most important revelations is the effect of toxic metals on reproductive system in mammals. In the preceding section we attempted to provide a recent and clear glimpse in all aspects regarding arsenic toxicity on reproduction in mammals. It is the most important concern that should be explored for better understanding and seeking preventive measures to get rid of this striking issue. Arsenic is an important environmental toxicant that affects the reproductive system of mammals. These toxic effects are influenced by variant sources and routes as well as doses and periods of exposure. Integration of novel information on the formation of fate and actions of arsenic toxicity in human and animal population and other species will reduce the uncertainties in the risk assessment for arsenic. This effort would help to protect the public health against the toxic and carcinogenic effects associated with arsenic exposure.
Authors are thankful to anonymous reviewers for their valuable comments in critical review of the manuscript.
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